BIS study reveals major gap in Bitcoin on‑chain transfer estimates

Researchers at the Bank for International Settlements (BIS) have highlighted a substantial discrepancy in how Bitcoin on‑chain transfer values are measured. Depending on the methodology used, estimates can differ by as much as sixfold, raising concerns about the reliability of commonly cited crypto metrics.

Why Bitcoin transfer estimates vary

The gap stems from Bitcoin’s transaction structure. When a user spends Bitcoin, any unspent portion is often returned to the sender as a change output. Some measurement approaches count this change as a separate transfer, inflating the total on‑chain value despite the funds not moving to a third party.

Implications for other metrics

The study also notes that Bitcoin’s market capitalization can be overstated, at times reaching four times the realized capitalization that values each coin at the price of its last movement. Similar measurement challenges were observed across Ethereum and Tron, where smart‑contract activity and token usage patterns complicate straightforward quantification.

Stablecoin measurement challenges

Stablecoins such as USDT exhibit divergent use cases across blockchains. On Ethereum, USDT is heavily tied to DeFi activity, with over 20% held by smart contracts in 2022, whereas on Tron it is primarily used for payments and store‑of‑value purposes, with roughly 1% in smart contracts. Aggregating activity across chains can therefore mask distinct economic functions.

Industry responses

Analytics providers are already adjusting raw blockchain data to better reflect genuine economic activity. Visa’s Onchain Analytics dashboard, for example, separates total stablecoin transaction volume from an adjusted figure that excludes high‑frequency trading, bots, bridge routing, and internal exchange operations. Over the past 30 days, Visa reported $6.4 trillion in total stablecoin volume versus $313.1 billion in adjusted volume.

Conclusion

The BIS researchers caution that on‑chain indicators should be treated as “noisy approximations rather than direct measures of economic activity,” underscoring the need for refined analytics when assessing the crypto ecosystem.